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Results for “statistical inference”

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PubMed2002

Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals.

There have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype. A regression-based approach is then used to relate inferred haplotype probabilities to the response. The relationship of this technique to commonly used approaches developed for case-control data is discussed. We confirm the proper size of the test under H(0) and find an increase in power under the alternative by comparing test results using inferred haplotypes with single-marker tests using simulated data. More importantly, analysis of real data comprised of a dense map of single nucleotide polymorphisms spaced along a 12-cM chromosomal region allows us to confirm the utility of the haplotype approach as well as the validity and usefulness of the proposed statistical technique. The method appears to be successful in relating data from multiple, correlated markers to response.

Algorithms↗
PubMed2006

Statistical inference and forensic evidence: evaluating a bullet lead match.

This experiment tested the ability of undergraduate mock jurors (N=295) to draw appropriate conclusions from statistical data on the diagnostic value of forensic evidence. Jurors read a summary of a homicide trial in which the key evidence was a bullet lead "match" that was either highly diagnostic, non-diagnostic, or of unknown diagnostic value. There was also a control condition in which the forensic "match" was not presented. The results indicate that jurors as a group used the statistics appropriately to distinguish diagnostic from non-diagnostic forensic evidence, giving considerable weight to the former and little or no weight to the latter. However, this effect was attributable to responses of a subset of jurors who expressed confidence in their ability to use statistical data. Jurors who lacked confidence in their statistical ability failed to distinguish highly diagnostic from non-diagnostic forensic evidence; they gave no weight to the forensic evidence regardless of its diagnostic value. Confident jurors also gave more weight to evidence of unknown diagnostic value. Theoretical and legal implications are discussed.

Female↗
PubMed2005

Bayesian statistical inference in psychology: comment on Trafimow (2003).

D. Trafimow (2003) presented an analysis of null hypothesis significance testing (NHST) using Bayes's theorem. Among other points, he concluded that NHST is logically invalid, but that logically valid Bayesian analyses are often not possible. The latter conclusion reflects a fundamental misunderstanding of the nature of Bayesian inference. This view needs correction, because Bayesian methods have an important role to play in many psychological problems where standard techniques are inadequate. This comment, with the help of a simple example, explains the usefulness of Bayesian inference for psychology.

Bayes Theorem↗
PubMed1990

Quantified neurophysiology with mapping: statistical inference, exploratory and confirmatory data analysis.

Topographic mapping of brain electrical activity has become a commonly used method in the clinical as well as research laboratory. To enhance analytic power and accuracy, mapping applications often involve statistical paradigms for the detection of abnormality or difference. Because mapping studies involve many measurements and variables, the appearance of a large data dimensionality may be created. If abnormality is sought by statistical mapping procedures and if the many variables are uncorrelated, certain positive findings could be attributable to chance. To protect against this undesirable possibility we advocate the replication of initial findings on independent data sets. Statistical difference attributable to chance will not replicate, whereas real difference will reproduce. Clinical studies must, therefore, provide for repeat measurements and research studies must involve analysis of second populations. Furthermore, Principal Components Analysis can be employed to demonstrate that variables derived from mapping studies are highly intercorrelated and data dimensionality substantially less than the total number of variables initially created. This reduces the likelihood of capitalization on chance. The need to constrain alpha levels is not necessary when dimensionality is low and/or a second data set is available. When only one data set is available in research applications, techniques such as the Bonferroni correction, the "leave-one-out" method, and Descriptive Data Analysis (DDA) are available. These techniques are discussed, clinical and research examples are given, and differences between Exploratory (EDA) and Confirmatory Data Analysis (EDA) are reviewed.

Brain↗
PubMed2002

p values for pathogens: statistical inference from infectious-disease data.

Certain features of infectious-disease data-including the aggregated nature of the data, confounding variables, correlated variables, and non-linear relations-complicate the use of standard statistical procedures. Using data on a helminth infection, we review the use of three parametric tests (analysis of variance, linear regression, and logistic regression), address the complications arising from violation of the assumptions for these tests, and suggest methods of correction. We also compare the relative merits of parametric methods with equivalent non-parametric approaches, and illustrate the differences produced with results from a Kruskal-Wallis test and a t test. The value of using a resampling method-bootstrapping-is also shown. Finally, we discuss problems arising from use of a study design that requires data on the same attribute to be collected from the same individual over a period of time, and present three methods for overcoming this complication, showing that, in the example used, the mixed effect model and generalised estimating equation give similar results.

Analysis of Variance↗